NoTube – making TV a medium for personalized interaction

In this paper, we introduce NoTube’s vision on deploying semantics in interactive TV context in order to contextualize distributed applications and lift them to a new level of service that provides context-dependent and personalized selection of TV content. Additionally, lifting content consumption from a single-user activity to a community-based experience in a connected multi-device environment is central to the project. Main research questions relate to (1) data integration and enrichment - how to achieve unified and simple access to dynamic, growing and distributed multimedia content of diverse formats? (2) user and context modeling - what is an appropriate framework for context modeling, incorporating task-, domain and device-specific viewpoints? (3) context-aware discovery of resources - how could rather fuzzy matchmaking between potentially infinite contexts and available media resources be achieved? (4) collaborative architecture for TV content personalization - how can the combined information about data, context and user be put at disposal of both content providers and end-users in the view of creating extremely personalized services under controlled privacy and security policies? Thus, with the grand challenge in mind - to put the TV viewer back in the driver's seat – we focus on TV content as a medium for personalized interaction between people based on a service architecture that caters for a variety of content metadata, delivery channels and rendering devices

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

In general, recommendation can be viewed as a matching problem, i.e., match proper items for proper users. However, due to the huge semantic gap between users and items, it's almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i.e., representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i.e., the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework

A deep learning-based hybrid model for recommendation generation and ranking

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets

Visitors' satisfaction with heritage sites in New Zealand: Causes and complexities, clusters and causes

The thesis presents findings from a survey of over 1,000 visitors at three New Zealand heritage sites. These sites were Te Puia, the Rotorua Bathhouse Museum and the Rangiriri Battlefield Interpretation Centre. All three represent a key period in New Zealand’s history from the period of approximately 1840 to 1900, but in the case of Te Puia there is also a continuing contemporary cultural importance. This last site, located in Rotorua, was founded as the Maori Arts and Crafts Institute and was established to perpetuate Maori tradition skills in areas such as carving and weaving. Its location was in part determined by the volcanic nature of the valley, long inhabited by members of Te Arawa tribal people. The site has a strong connection with tourism as Te Arawa have entertained tourists from the mid-nineteenth century in the volcanic area. The site therefore represents a tourism site from the perspective of history, culture and natural heritage. The Bathhouse Museum represents a period of late colonial architecture while the third site, the Rangiriri Battlefield is based on the remnants of the Pa (Maori fortifications) that was the site of a battle between the colonial government forces and the Maori Kingi movement on November 23rd 1840. The motive for the research was to provide a profile of visitors for the respective sites and their management, and then to assess to what degree socio-demographics might be explanatory variables in determining future visitation. The core theories being employed revolved around concepts of levels of interest in heritage and historic sites, the intellectual search for knowledge, and the degree to which people became involved in the activity of heritage site visitation. The work was driven by the finding that only about 11 per cent of visits to cultural tourism sites were ‘purposeful’ tourists as defined by McKercher and Du Cros (2002). Being purposeful implies having specific degrees of interest, of becoming involved and possibly seeking meanings that implied senses of identity. That is, self-awareness accrued from having a better understanding of the past as a means of knowing about the present. This conceptualisation implies use of the theories of involvement, benefits and self-awareness, and the managerial aspects of interpretation. Normally such an approach has been seen by many researchers as a determinant of satisfaction, but in this thesis satisfaction is not seen as simply an end to a process. Rather, this thesis argues that to be satisfied entails not only cognitive and affective components, but also the conative. That conative component can include making recommendations to others, making visits to other heritage sites, or joining organisations associated with heritage sites such as the New Zealand Historic Place Trust. These form key themes in the literature review. Unfortunately, while these premises emerged from the literature review and informed the hypotheses that are later described in the thesis, they were not wholly supported by the data. It is suggested that one reason for this, from a statistical perspective, was that measures used were subject to multi-collinearity and auto-correlation – put simply, many of the variables are not independent from each other. For example, it is suggested that satisfaction is actually enhanced by subsequently being able to make recommendations to friends and others; that the act of making a recommendation enhances one’s own self in both the eyes of that friend or through an enhanced self- perception of being helpful, and thus auto-correlation may exist between these variables. This realisation thus leads, in the conclusions of Chapter Eight, to new suggestions for potential future researchers concerning ways of looking at the nature of involvement that draw on distinctions between situational and enduring involvement. Finally, it also needs to be noted that tourists are not lay historians, but are the makers of their holidays, and hence the debate is contextualised within the act of being on holiday, which itself is a period of escape and relaxation for many. Hence the relationships being examined in this thesis are complex, interactive and yet rewarding to untangle

Tag based Bayesian latent class models for movies : economic theory reaches out to big data science

For the past 50 years, cultural economics has developed as an independent research specialism. At its core are the creative industries and the peculiar economics associated with them, central to which is a tension that arises from the notion that creative goods need to be experienced before an assessment can be made about the utility they deliver to the consumer. In this they differ from the standard private good that forms the basis of demand theory in economic textbooks, in which utility is known ex ante. Furthermore, creative goods are typically complex in composition and subject to heterogeneous and shifting consumer preferences. In response to this, models of linear optimization, rational addiction and Bayesian learning have been applied to better understand consumer decision- making, belief formation and revision. While valuable, these approaches do not lend themselves to forming verifiable hypothesis for the critical reason that they by-pass an essential aspect of creative products: namely, that of novelty. In contrast, computer sciences, and more specifically recommender theory, embrace creative products as a study object. Being items of online transactions, users of creative products share opinions on a massive scale and in doing so generate a flow of data driven research. Not limited by the multiple assumptions made in economic theory, data analysts deal with this type of commodity in a less constrained way, incorporating the variety of item characteristics, as well as their co-use by agents. They apply statistical techniques supporting big data, such as clustering, latent class analysis or singular value decomposition. This thesis is drawn from both disciplines, comparing models, methods and data sets. Based upon movie consumption, the work contrasts bottom-up versus top-down approaches, individual versus collective data, distance measures versus the utility-based comparisons. Rooted in Bayesian latent class models, a synthesis is formed, supported by the random utility theory and recommender algorithm methods. The Bayesian approach makes explicit the experience good nature of creative goods by formulating the prior uncertainty of users towards both movie features and preferences. The latent class method, thus, infers the heterogeneous aspect of preferences, while its dynamic variant- the latent Markov model - gets around one of the main paradoxes in studying creative products: how to analyse taste dynamics when confronted with a good that is novel at each decision point. Generated by mainly movie-user-rating and movie-user-tag triplets, collected from the Movielens recommender system and made available as open data for research by the GroupLens research team, this study of preference patterns formation for creative goods is drawn from individual level data

Balanced Order Batching with Task-Oriented Graph Clustering

Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive process and, by using good batching methods, substantial savings can be obtained. The BOBP is a NP-hard combinational optimization problem and designing a good problem-specific heuristic under the quasi-real-time system response requirement is non-trivial. In this paper, rather than designing heuristics, we propose an end-to-end learning and optimization framework named Balanced Task-orientated Graph Clustering Network (BTOGCN) to solve the BOBP by reducing it to balanced graph clustering optimization problem. In BTOGCN, a task-oriented estimator network is introduced to guide the type-aware heterogeneous graph clustering networks to find a better clustering result related to the BOBP objective. Through comprehensive experiments on single-graph and multi-graphs, we show: 1) our balanced task-oriented graph clustering network can directly utilize the guidance of target signal and outperforms the two-stage deep embedding and deep clustering method; 2) our method obtains an average 4.57m and 0.13m picking distance ("m" is the abbreviation of the meter (the SI base unit of length)) reduction than the expert-designed algorithm on single and multi-graph set and has a good generalization ability to apply in practical scenario.Comment: 10 pages, 6 figure

Is operational research in UK universities fit-for-purpose for the growing field of analytics?

Over the last decade considerable interest has been generated into the use of analytical methods in organisations. Along with this, many have reported a significant gap between organisational demand for analytical-trained staff, and the number of potential recruits qualified for such roles. This interest is of high relevance to the operational research discipline, both in terms of raising the profile of the field, as well as in the teaching and training of graduates to fill these roles. However, what is less clear, is the extent to which operational research teaching in universities, or indeed teaching on the various courses labelled as analytics , are offering a curriculum that can prepare graduates for these roles. It is within this space that this research is positioned, specifically seeking to analyse the suitability of current provisions, limited to master s education in UK universities, and to make recommendations on how curricula may be developed. To do so, a mixed methods research design, in the pragmatic tradition, is presented. This includes a variety of research instruments. Firstly, a computational literature review is presented on analytics, assessing (amongst other things) the amount of research into analytics from a range of disciplines. Secondly, a historical analysis is performed of the literature regarding elements that can be seen as the pre-cursor of analytics, such as management information systems, decision support systems and business intelligence. Thirdly, an analysis of job adverts is included, utilising an online topic model and correlations analyses. Fourthly, online materials from UK universities concerning relevant degrees are analysed using a bagged support vector classifier and a bespoke module analysis algorithm. Finally, interviews with both potential employers of graduates, and also academics involved in analytics courses, are presented. The results of these separate analyses are synthesised and contrasted. The outcome of this is an assessment of the current state of the market, some reflections on the role operational research make have, and a framework for the development of analytics curricula. The principal contribution of this work is practical; providing tangible recommendations on curricula design and development, as well as to the operational research community in general in respect to how it may react to the growth of analytics. Additional contributions are made in respect to methodology, with a novel, mixed-method approach employed, and to theory, with insights as to the nature of how trends develop in both the jobs market and in academia. It is hoped that the insights here, may be of value to course designers seeking to react to similar trends in a wide range of disciplines and fields

Use of a formal consensus development technique to produce recommendations for improving the effectiveness of adult mental health multidisciplinary team meetings

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Multidisciplinary team (MDT) meetings are the core mechanism for delivering mental health care but it is unclear which models improve care quality. The aim of the study was to agree recommendations for improving the effectiveness of adult mental health MDT meetings, based on national guidance, research evidence and experiential insights from mental health and other medical specialties. METHODS: We established an expert panel of 16 health care professionals, policy-makers and patient representatives. Five panellists had experience in a range of adult mental health services, five in heart failure services and six in cancer services. Panellists privately rated 68 potential recommendations on a scale of one to nine, and re-rated them after panel discussion using the RAND/UCLA Appropriateness Method to determine consensus. RESULTS: We obtained agreement (median ≥ 7) and low variation in extent of agreement (Mean Absolute Deviation from Median of ≤1.11) for 21 recommendations. These included the explicit agreement and auditing of MDT meeting objectives, and the documentation and monitoring of treatment plan implementation. CONCLUSIONS: Formal consensus development methods that involved learning across specialities led to feasible recommendations for improved MDT meeting effectiveness in a wide range of settings. Our findings may be used by adult mental health teams to reflect on their practice and facilitate improvement. In some other contexts, the recommendations will require modification. For example, in Child and Adolescent Mental Health Services, context-specific issues such as the role of carers should be taken into account. A limitation of the comparative approach adopted was that only five members of the panel of 16 experts were mental health specialists.This report presents independent research commissioned by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, MRC, CCF, NETSCC, the Health Services and Delivery Research programme or the Department of Health